Number Of Sequences Research Articles

VISTA: A Tool for Fast Taxonomic Assignment of Viral Genome Sequences.

The rapid expansion of the number of viral genome sequences in public databases necessitates a scalable, universal, and automated preliminary taxonomic framework for comprehensive virus studies. Here, we introduce VISTA (Virus Sequence-based Taxonomy Assignment), a computational tool that employs a novel pairwise sequence comparison system and an automatic demarcation threshold identification framework for virus taxonomy. Leveraging physio-chemical property sequences, k-mer profiles, and machine learning techniques, VISTA constructs a robust distance-based framework for taxonomic assignment. Functionally similar to PASC (Pairwise Sequence Comparison), a widely used virus assignment tool based on pairwise sequence comparison, VISTA demonstrates superior performance by providing significantly improved separation for taxonomic groups, more objective taxonomic demarcation thresholds, greatly enhanced speed, and a wider application scope. We successfully applied VISTA to 38 virus families, as well as to the class Caudoviricetes. This demonstrates VISTA's scalability, robustness, and ability to automatically and accurately assign taxonomy to both prokaryotic and eukaryotic viruses. Furthermore, the application of VISTA to 679 unclassified prokaryotic virus genomes recovered from metagenomic data identified 46 novel virus families. VISTA is available as both a command line tool and a user-friendly web portal at https://ngdc.cncb.ac.cn/vista.

A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach. The training dataset consisted of RNA sequencing data from 6 samples. Multiple natural language processing models were examined, differing in the type of dictionary components (word length, step, context) as well as the encoding length and number of sequences required for algorithm training. The best configuration analyses the entire sequence and uses a word length of 3 base pairs with one-word neighbor on each side. For the considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and NPV was 99.25%, compared to minimap2's performance in a cross-validation scenario. The next stage focused on exploring the dictionary components and attempting to optimize it, employing statistical techniques as well as those relying on the explainability of the decisions made. Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to 98.15%. Obtained model, validated on an external independent genome sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced dictionary. The salmon-estimated read counts differed from the classical approach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one. We conclude that for long Oxford nanopore reads, a natural language processing-based approach can reliably replace classical mapping when there is a need for fast, reliable and energy and computationally efficient targeted mapping of a pre-defined subset of transcripts. The developed model can be easily retrained to identify selected transcripts and/or work with various long-read sequencing techniques. Our results of the study clearly demonstrate the potential of applying techniques known from classical text processing to nucleotide sequences.

A Study of Various Clustering Algorithms Used for Radar Signal Sorting

Radar signal sorting plays a significant part in radar countermeasure technology and reconnaissance systems. By means of radar signal sorting, several radars and their parameters in the battlefield are precisely recognized and placed in the radar records for subsequent positioning and jamming processing. The basic sorting methods cannot fulfill the sorting process with accurate and efficient results. Therefore, in this paper, we conduct a study on two main classes clustering techniques, the first is hierarchical based clustering, and the second one is partition based clustering, which have different characteristics into groups of pulses and they can sort and handle large number of radar sequence with high precision and accuracy. The numerical simulations studied and compared both methods under different perspectives to clarify a new directions based on the insightful investigations of these sorting techniques.

Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization

Fluorescence in Situ Hybridization (FISH) is a technique for macromolecule identification that utilizes the complementarity of DNA or DNA/RNA double strands. Probes, crafted from selected DNA strands tagged with fluorophore-coupled nucleotides, hybridize to complementary sequences within the cells and tissues under examination. These are subsequently visualized through fluorescence microscopy or imaging systems. However, the vast number of cells and disorganized nucleic acid sequences in FISH images present significant challenges. The manual processing and analysis of these images are not only time-consuming but also prone to human error due to visual fatigue. To overcome these challenges, we propose the integration of medical imaging with deep learning to develop an automated detection system for FISH images. This system features an algorithm capable of quickly detecting fluorescent spots and capturing their coordinates, which is crucial for evaluating cellular characteristics in cancer diagnosis. Traditional models struggle with the small size, low resolution, and noise prevalent in fluorescent points, leading to significant performance declines. This paper offers a detailed examination of these issues, providing insights into why traditional models falter. Comparative tests between the YOLO series models and our proposed method affirm the superior accuracy of our approach in identifying fluorescent dots in FISH images.

A proactive defense method against eavesdropping attack in SDN-based storage environment

The integration of Software-Defined Networking (SDN) in storage centers aims to enhance storage performance. However, this integration also introduces new concerns, particularly the potential eavesdropping attacks that pose a substantial risk to data privacy. By issuing flow tables (e.g., via compromised SDN switches), attackers can conveniently collect target traffic and extract confidential information with session reassembly methods. To proactively mitigate such attacks by preventing session reassembly, various moving target defense methods, such as end hopping, have been proposed. However, this study uncovers several deficiencies within existing end hopping methods. To address these deficiencies, we propose a novel linkage-field-based self-synchronizing end hopping method, which obfuscates end information (e.g., IP, Port) and linkage fields (e.g., sequence number and ID number) without third-party assistance. Furthermore, to counter the potential invalidation of end hopping methods resulting from brute-force reassembly of a small number of sessions, we propose a fake segment injection method. Extensive experiments have been conducted both in simulation and real-world environment to evaluate the effectiveness of our proposed methods. The results demonstrate that our proposed methods can effectively defend against eavesdropping attacks with acceptable performance overhead.

DIFFERENCE DOUBLE SEQUENCES OF BI-COMPLEX NUMBERS

In this article, we have introduced the notion of convergence of difference double sequences in Pringsheim’s sense, difference null in Pringsheim’s sense, bounded difference, bounded convergence differ ence, bounded null difference, regular convergence difference and reg ular null difference double sequences of bi-complex numbers. We have proved that these are linear spaces. With the help of the Euclidean norm defined on bi-complex numbers, we have established their differ ent algebraic and topological properties, as well as some of their geo metric properties. Suitable examples have been discussed to support the introduction of these classes of sequences and during the investi gation of their properties for failure cases.

On a union of homogeneous sequences

A sequence of positive integers is said to be quasi-primitive if there do not exist three distinct integers such that the greatest common divisor of two elements is not the third. It is clear that the sequence of prime numbers is quasi-primitive. The Erdős quasi-primitive conjecture states that the sum of the reciprocal of the product of an element by its logarithm over any quasi-primitive sequence is maximized by the sum over the sequence of all powers of prime numbers. Note that Erdős, before introduce his conjecture, showed that for any quasi-primitive sequence different from one, this sum is convergent and bounded by a real constant. In this article, by studying the analogous Erdős conjecture, we prove that when the real is sufficiently large, there exists a quasi-primitive sequence such that the sum of the reciprocal of the product of an element by the sum of its logarithm and , over this quasi-primitive sequence is very large than the sum over the sequence of all powers of prime numbers. Obviously, if the case where equals zero is also true, then the conjecture is false.

Major Histocompatibility Complex Class II Genes Allele Diversity in Landlocked Seals.

The allelic diversity of exon 2 (DQB gene) and exon 3 (DRB gene) of major histocompatibility complex class II was studied for the first time in two species of the landlocked pinnipeds, Baikal (N = 79) and Caspian (N = 32) seals, and these were in compared with the widespread Arctic species, the ringed seal (N = 13). The analysis of the second exon comprising the antigen-binding region revealed high allelic diversity in all three species but the pattern of the diversity was the most specific for the Baikal seal. This species differs from the other two by the smallest number of alleles in the population, yet they have the largest number of alleles per individual and by the maximum similarity of individual genotypes. Presumably, this specificity is a consequence of the spatial and temporal homogeneity of the Lake Baikal environment. Analysis of the third exon encoding the conserved β2-domain showed that the Baikal seal differs by the greatest number of amino acid sequences per individual, while the Caspian seal has the lowest number of variants. A single variant of the β2-domain, the same as in the ringed seal, predominates in the Caspian seal, whereas in the Baikal seal the two other variants predominate. At the same time, three species-specific amino acid sequences were observed among minor variants in the Caspian seal, while only one was found in the Baikal seal. This fact may suggest a longer period of independent evolution in the Caspian seal compared to the Baikal seal.

Using the RNAstructure Software Package to Predict Conserved RNA Structures.

The structures of many non-coding RNAs (ncRNA) are conserved by evolution to a greater extent than their sequences. By predicting the conserved structure of two or more homologous sequences, the accuracy of secondary structure prediction can be improved as compared to structure prediction for a single sequence. Here, we provide protocols for the use of four programs in the RNAstructure suite topredict conserved structures: Multilign, TurboFold, Dynalign, and PARTS. TurboFold iteratively aligns multiple homologous sequences and estimates the pairing probabilities for the conserved structure. Dynalign, PARTS, and Multilign are dynamic programming algorithms that simultaneously align sequences and identify the common secondary structure. Dynalign uses a pair of homologs and finds the lowest free energy common structure. PARTS uses a pair of homologs and estimates pairing probabilities from the base pairing probabilities estimated for each sequence. Multilign uses two or morehomologs and finds the lowest free energy common structure using multiple pairwise calculations with Dynalign. It scales linearly with the number of sequences. We outline the strengths of each program. These programs can be run through web servers, on the command line, or with graphical user interfaces. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Predicting a structure conserved in three or more sequences with the RNAstructure web server Basic Protocol 2: Predicting a structure conserved in two sequences with the RNAstructure web server Alternative Protocol 1: Predicting a structure conserved in multiple sequences in the RNAstructure graphical user interface Alternative Protocol 2: Predicting a structure conserved in two sequences with Dynalign in the RNAstructure graphical user interface Alternative Protocol 3: Running TurboFold on the command line.

Recursive averages and the renewal theorem

Consider the sequence of numbers an defined by the iteration (1) for n ≥ k, where pr 1 ≤ r ≤ k) are non-negative numbers with and the starting values are given. So an is a weighted average of the previous k terms.

Comparative analysis of chloroplast genomes in ten holly (Ilex) species: insights into phylogenetics and genome evolution

In order to clarify the chloroplast genomes and structural features of ten Ilex species and provide insights into the phylogeny and genome evolution of the genus Ilex, we conducted a comparative analysis of chloroplast genomes using bioinformatics methods. The chloroplast genomes of ten Ilex species were obtained, and their structural features and variations were compared. The results indicated that all chloroplast genomes in the genus Ilex exhibit a double-stranded circular structure, with sizes ranging from 157,356 to 158,018 bp, showing minimal differences in size. The chloroplast genomes of the ten Ilex species have a relatively conservative gene count, with a total of 134 to 135 genes, including 88 or 89 protein-coding genes, and a conserved number of 8 rRNA genes. Each chloroplast genome contains 3 to 123 SSR (Simple Sequence Repeat) sites, predominantly composed of mononucleotide and trinucleotide repeats, with no detection of pentanucleotide or hexanucleotide repeats. The variation in dispersed repeat sequences among Ilex species is minimal, with a total repeat sequence number ranging from 1 to 14, concentrated in the length range of 30 to 42 base pairs. The expansion and contraction of chloroplast genome boundaries among Ilex species are relatively stable, with only minor variations observed in individual species. Variations in non-coding regions are more pronounced than those in coding regions, with the variability in the Large Single Copy region (LSC) being the highest, while the variability in the Inverted Repeat region A (IRa) is the lowest. The divergence time among Ilex species was estimated using the MCMC-tree module, revealing the evolutionary relationships among these species, their common ancestors, and their differentiation throughout the evolutionary process. The research findings provide a valuable reference for the systematic study and molecular marker development of Ilex plants.

Arbitrary Random Variables and Wiman’s Inequality

We study the class of random entire functions given by power series, in which the coefficients are formed as the product of an arbitrary sequence of complex numbers and two sequences of random variables. One of them is the Rademacher sequence, and the other is an arbitrary complex-valued sequence from the class of sequences of random variables, determined by a certain restriction on the growth of absolute moments of a fixed degree from the maximum of the module of each finite subset of random variables. In the paper we prove sharp Wiman–Valiron’s type inequality for such random entire functions, which for given p∈(0;1) holds with a probability p outside some set of finite logarithmic measure. We also considered another class of random entire functions given by power series with coefficients, which, as above, are pairwise products of the elements of an arbitrary sequence of complex numbers and a sequence of complex-valued random variables described above. In this case, similar new statements about not improvable inequalities are also obtained.

Analisis Kemampuan Mahasiswa Matematika FMIPA Unimed dalam Menyelesaikan Permasalahan Konvergensi dan Divergensi Barisan Bilangan Real dengan Berbantuan Software MATLAB

This research aims to analyze the ability of FMIPA Mathematics students at Medan State University (Unimed) in solving problems of convergence and divergence of real number sequences, as well as evaluating the effectiveness of using MATLAB software in supporting understanding of these concepts. This research is based on the aim of overcoming students' difficulties in understanding the concepts of convergence and divergence. The research method used was qualitative, with a sample of 20 students selected using purposive sampling. Data was collected through written tests and observations of the use of MATLAB which were distributed via Google Form in solving questions. The results show that 55% of students are in the "very understanding" category, with MATLAB proving to be very effective in making problem solving easier and deepening understanding.

Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.

Enhanced Chaotic Pseudorandom Number Generation Using Multiple Bernoulli Maps with Field Programmable Gate Array Optimizations

Certain methods for implementing chaotic maps can lead to dynamic degradation of the generated number sequences. To solve such a problem, we develop a method for generating pseudorandom number sequences based on multiple one-dimensional chaotic maps. In particular, we introduce a Bernoulli chaotic map that utilizes function transformations and constraints on its control parameter, covering complementary regions of the phase space. This approach allows the generation of chaotic number sequences with a wide coverage of phase space, thereby increasing the uncertainty in the number sequence generation process. Moreover, by incorporating a scaling factor and a sine function, we develop a robust chaotic map, called the Sine-Multiple Modified Bernoulli Chaotic Map (SM-MBCM), which ensures a high degree of randomness, validated through statistical mechanics analysis tools. Using the SM-MBCM, we propose a chaotic PRNG (CPRNG) and evaluate its quality through correlation coefficient analysis, key sensitivity tests, statistical and entropy analysis, key space evaluation, linear complexity analysis, and performance tests. Furthermore, we present an FPGA-based implementation scheme that leverages equivalent MBCM variants to optimize the electronic implementation process. Finally, we compare the proposed system with existing designs in terms of throughput and key space.

Analysis of Powassan Virus Genome Sequences from Human Cases Reveals Substantial Genetic Diversity with Implications for Molecular Assay Development

Powassan virus (POWV) is an emerging tick-borne virus that causes severe meningoencephalitis in the United States, Canada, and Russia. Serology is generally the preferred diagnostic modality, but PCR on cerebrospinal fluid, blood, or urine has an important role, particularly in immunocompromised patients who are unable to mount a serologic response. Although the perceived poor sensitivity of PCR in the general population may be due to the biology of infection and health-seeking behavior (with short viremic periods that end before hospital presentation), limitations in assay design may also contribute. Genome sequences from clinical POWV cases are extremely scarce; PCR assay design has been informed by those available, but the numbers are limited. Larger numbers of genome sequences from tick-derived POWV are available, but it is not known if POWV genomes from human infections broadly mirror genomes from tick hosts, or if human infections are caused by a subset of more virulent strains. We obtained viral genomic data from 10 previously unpublished POWV human infections and showed that they broadly mirror the diversity of genome sequences seen in ticks, including all three major clades (lineage I, lineage II Northeast, and lineage II Midwest). These newly published clinical POWV genome sequences include the first confirmed lineage I infection in the United States, highlighting the relevance of all clades in human disease. An in silico analysis of published POWV PCR assays shows that many assays were optimized against a single clade and have mismatches that may affect their sensitivity when applied across clades. This analysis serves as a launching point for improved PCR design for clinical diagnostics and environmental surveillance.

Modified FOLFOX6 with Cetuximab versus with Radiotherapy in Neoadjuvant Treatment of Locally Advanced Rectal Cancer: A Single-Center, Prospective, Randomized Controlled Trial.

In this trial, the feasibility and efficacy of neoadjuvant chemotherapy with targeted agents in the treatment of patients with locally advanced rectal cancer were evaluated. In this single-center, prospective, randomized controlled trial, we randomly assigned (1 : 1) patients with locally advanced rectal cancer with wild-type RAS/BRAF gene to two groups: 5 cycles of modified leucovorin calcium (folinic acid), fluorouracil, and oxaliplatin combination regimen (modified FOLFOX6, mFOLFOX6) concurrent with 25 times radiotherapy or 5 cycles of mFOLFOX6 plus cetuximab, all with subsequent total mesorectal excision (TME) resection and adjuvant chemotherapy. We performed a random assignment by a computer-generated random number sequence. The primary end point was the R0 resection rate. The secondary end points were rates of pathologic complete response, downstaging, adverse events, postoperative complications, preventive enterostomy and low anterior resection syndrome. From January 6, 2020 to October 28, 2022, 80 patients were assigned and evaluated. In the mFOLFOX6-RT and mFOLFOX6-Cet groups, the rate of R0 resection was 96.7 and 96.9% (p = 1.000); the rate of pathological complete response (pCR) was 23.3 and 21.9% (p = 0.891); and the rate of downstaging (ypStage 0 to 1) was 53.3 and 53.1% (p = 1.000), respectively. No statistical differences between the two groups were observed in the incidence of adverse events and postoperative complications. Additionally, lower rates of preventive enterostomy and low anterior resection syndrome were shown in the mFOLFOX6-Cet group compared to the mFOLFOX6-RT group. The neoadjuvant treatment strategy of mFOLFOX6 with cetuximab is feasible and promising for patients with locally advanced rectal cancer, even superior to mFOLFOX6 with radiotherapy.

ILMCNet: A Deep Neural Network Model That Uses PLM to Process Features and Employs CRF to Predict Protein Secondary Structure.

Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research. We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage. To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (CRF). Protein language models (PLMs) pre-trained on sequences from multiple large databases can provide sequence features that incorporate evolutionary information. ILMCNet uses positional encoding to ensure that the input features include positional information. To better utilize these features, we propose a hybrid network architecture that employs a Transformer Encoder to enhance features and integrates a feature extraction module combining a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM). This design enables deep extraction of localized features while capturing global bidirectional information. In the prediction stage, ILMCNet employs CRF to capture the interdependencies between secondary structures. Experimental results on benchmark datasets such as CB513, TS115, NEW364, CASP11, and CASP12 demonstrate that the prediction performance of our method surpasses that of comparable approaches. This study proposes a new approach to PSSP research and is expected to play an important role in other protein-related research fields, such as protein tertiary structure prediction.

Bat (Chiroptera) viruses of Russia and neighboring countries

Aim. To analyze the diversity of bat viruses in Russia and neighboring countries. To identify the main vectors of these pathogens and possible ways of introduction into the territory of the Russian Federation.In Russia the RNA sequences of 36 Coronaviridae, 8 Rhabdoviridae and 3 Astroviridae have been obtained from bats. In neighboring countries, RNA/DNA sequences of viruses in bats have been found in Norway (2), Finland 14), Poland 42), Ukraine (8), Georgia (46), Kazakhstan (29), China (5.645) and Japan (109). The dsequences which have been discovered belong to 30 families (Table 1). An increase in the number of sequences obtained over the past year has been observed in Russia and China. The absolute majority of sequences belong to the Coronaviridae family (62.96 %).The available data on bat viruses in Russia indicate the need to develop and implement a state interdisciplinary programme dedicated to this problem. The greatest danger is posed by Rhabdoviridae: mortalities after infection have been recorded in Russia. The data obtained also indicate that the main vectors of viruses are representatives of the genera Eptesicus, Myotis, Nyctalus, Rhinolophus and Vespertilio. The risk of virus exchange and transmission is most likely to be between the western part of Russia and European countries.

A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.